Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor−liquid−solid fluidized bed evaporator

Abstract

The dynamics of vapor−liquid−solid (V−L−S) flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior, hindering accurate prediction of pressure drop signals. To address this challenge, this study proposes an innovative hybrid approach that integrates wavelet neural network (WNN) with chaos analysis. By leveraging the Cross-Correlation (C−C) method, the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN. Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals, advancing our understanding of the intricate dynamic phenomena occurring with V−L−S fluidized bed evaporators. Moreover, this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.